The vertex separation of a graph is the minimum vertex separation of a linear layout of that graph over all its linear layouts. A linear layout of a graph is an arrangement of its vertices in a line and the vertex separation of a linear layout is maximum number of vertices to the left of any intervertex "gap" that are adjacent to vertices to the right of that gap, over all gaps. A unicyclic graph is a connected graph with precisely one cycle that is, a tree plus an extra edge. We present a O(n lgn) algorithm to compute the optimal vertex separation of unicyclic graphs. The algorithm is "practical" in the sense that it is easily implementable. Furthermore, the algorithm outputs a layout for the unicyclic graph of minimum vertex separation.

A methodology is presented, by which atmospheric aerosol retrievals from a standard, elastic-scatter, lidar can be constrained by using information from coincident measurements from a high spectral resolution lidar (HSRL) or Raman lidar at a different wavelength. As high spectral resolution or inelastic-scattering lidars are now being incorporated coaxially into instruments with traditional, elastic-scatter channels at different wavelengths, a standard approach is needed to incorporate or fuse the diversity of spectral information so as to make maximal use of the aerosol measurements made from the elastic-scatter channel or channels. The approach is evaluated through simulation and with data from the NASA Langley Research Center Airborne HSRL instrument. The generality and extensibility of the method is also explored and discussed in the context of aerosol modeling. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License.

This thesis studies "the convoy-path interdiction problem" (CPIP) in which an interdictor uses limited resources to attack and disrupt road segments ("arcs") or road intersections ("nodes") in a road network in order to delay an adversary's convoy from reaching its destination. The convoy will move between a known origin node a and destination node b using a "quickest path." We first show how to compute, using an A* search, the convoy's quickest path under the assumptions that the convoy may occupy several arcs simultaneously, each arc may have a different speed limit, and the convoy maintains constant inter-vehicle spacing. The basic model assumes that the convoy moves in a single lane of traffic; an extension handles arcs that may have multiple lanes. Using that algorithm as a subroutine, a decomposition algorithm solves the optimal interdiction problem. Interdiction of a node or arc makes that node or arc impassable. Computational results are presented on grid networks with up to 629 nodes and 2452 arcs with varying levels of interdiction resource. Using Xpress-MP optimization software and a 2 GHz Pentium IV computer, the largest network problem solves in no more than 360 seconds given that at most 4 arcs can be interdicted.

This thesis presents an overarching plan to migrate a time-optimal spacecraft control algorithm from the MATLABTM development environment into an FPGA-based embedded-platform development board. Research at the Naval Postgraduate School has produced a revolutionary time-optimal spacecraft control algorithm based upon the Legendre Pseudospectral method. Currently, the control algorithm is dependent on the MATLABTM environment, a fourth generation language (4GL). 4GLs are powerful high-level abstraction and development tools, but are not efficiently instantiated into an embedded system. This study establishes three distinct development phases to migrate the algorithm from 4GL dependency to embedded operation. The first phase removes the algorithm's dependency on the 4GL environment by translating the algorithm into the C programming language. The second development phase compiles and embeds the algorithm into an FPGA-based development board. The final development phase introduces a custom computing machine (CCM) instantiated in an FPGA to reduce the control calculation time, thereby broadening the algorithm's potential application.

Boost-phase intercept of a threat intercontinental ballistic missile (ICBM) is the first layer of a multilayer defense. This thesis investigates the requirements and limitations of the U.S. space-based ICBM defense against North Korea, Iran and China by introducing an ICBM trajectory prediction, selecting an orbit for exoatmospheric kill vehicles (EKV) and developing a hybrid guidance algorithm. The prediction of the ICBM trajectory takes the rotation of the earth and the atmospheric drag into account along with the gravitational forces and thrust. The threat ICBM locations, specifications and capabilities of the EKV and EKV carrier, and the capabilities of the space launch vehicle are analyzed to determine an appropriate orbit for the space-based intercept. The pursuit guidance, proportional navigation guidance and bang-bang guidance rules and their performances are investigated and simulated for three example ICBM threats in three-dimensional environment. The simulation results performances are compared and analyzed for minimum miss distance, intercept time and total command effort. The guidance rules are combined to meet the mission requirements, resulting in a hybrid guidance algorithm, which uses different guidance rules for different stages of a boost-phase intercept scenario.

Efficient Designs (SEED) Center website (http://harvest.nps.edu).

In 2003, the US Army began using the Integrated Mechanical Diagnostics Health and Usage Management System (IMD-HUMS), an integrated airborne and ground-based system developed by Goodrich Corporation, to support maintenance of the UH-60L. IMD-HUMS is responsible for collecting, processing, analyzing, and storing an enormous amount of vibratory and flight regiime data obtained from sensors located throughout the aircraft. The purpose of this research is to predict failures of the UH-60L's electrical generators, applying Airtificial Neural Networks (ANN) on the IMD-HUMS-produced data. Artificial NNs are data based vice rule based, thereby possessing the potential capability to operate where analytical solutions are inadequate. They are reputed to be robust and highly tolerant of noisy data. Software tools such as Clementine 10.0, S-Plus 7.0, and Excel are used to establish these predictions. This research has verified that ANNs have a position in machinery condiiton monitoring and diagnostics. However, the limited nature of these results indicates that ANNs will not solve all machinery condition monitoring and diagnostics problems by themselves. They certainly will not completely replace conventional rule-based expert systems. Ultimately, it is anticipated that a symbiotic combination of these two technologies will provide the optimal solution to the machinery condition monitoring and diagnostics problem.

Reception of one or more signals, overlapping in frequency and time with the desired signal, is commonly called cochannel interference. Joint detection is the optimal minimum probability of error decision rule for cochannel interference. This dissertation investigates the optimum approach and a number of suboptimum approaches to joint detection when a priori information based in fields, or sets of transmitted symbols, is available. In the general case the solution presents itself as a time-varying estimation problem that may be efficiently solved with a modified Bahl, Cocke, Jelinek and Raviv (BCJR) algorithm. The low-entropy properties of a particular signal of interest, the Automatic Identifi- cation System (AIS), are presented. Prediction methods are developed for this signal to be used as a priori information for a joint field-based maximum a posteriori (MAP) detector. Advanced joint detection techniques to mitigate cochannel interference are found to have superior bit error rate (BER) performance than can be obtained compared to traditional methods.

Approved for public release; distribution is unlimited / Usage Monitoring requires accurate regime recognition. For each regime, there is a usage assigned for each component. For example, the damage accumulated at a component is higher if the aircraft is undergoing a high G maneuver than in level flight. The objective of this research is to establish regime recognition models using classification algorithms. The data used in the analysis are the parametric data collected by the onboard system and the actual data, consisting of the correct regime collected from the flight cards. This study uses Rpart (with a tree output) and C5.0 (with a ruleset output) to establish two different models. Before model fitting, the data was divided into smaller datasets that represent regime families by subsetting using important flight parameters. Nonnormal tolerance intervals are constructed on the uninteresting values; then these values in the interval are set to zero to be muted (e.g. excluded). These processes help reduce the effect of noise on classification. The final models had correct classification rates over 95%. The number of bad misclassifications were minimized (e.g. the number of bad misclassifications of a level flight regime as a hover regime was minimized), but the models were not as powerful in classifying the low-speed regimes as in classifying high-speed regimes. / Outstanding Thesis

The research presented in this thesis provides the reader with a set of algorithms and techniques that enable the user to remotely determine what chipset and device driver an 802.11 device is using. The work details both passive and active approaches, and quantitatively gauges the effectiveness of various techniques. The implications of this are far ranging. On one hand, the techniques can be used to implement innovative new features in Wireless Intrusion Detection Systems (WIDS). On the other, they can be used to target link layer device driver attacks with much higher precision.